Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother

Hydrology and Earth System Sciences

Volumn 16, Issue 2, 2012, Pages 287-304

  Bailey, R T ^a   Bau D ^a  

^a Veterinary Dentistry and Oral Surgery   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BASE-LINE CONDITIONS; DATA ASSIMILATION ALGORITHMS; ESTIMATION METHODOLOGIES; GEOSTATISTICAL MODELS; GROUNDWATER FLOW MODEL; PARAMETER ESTIMATION METHOD; SPATIOTEMPORAL VARIABILITY; WATER TABLE ELEVATION;

AQUIFERS; CATCHMENTS; DATA PROCESSING; ESTIMATION; GROUNDWATER FLOW; GROUNDWATER RESOURCES; HYDRAULIC CONDUCTIVITY; ITERATIVE METHODS; RUNOFF; SPATIAL DISTRIBUTION; STREAM FLOW;

PARAMETER ESTIMATION;

AQUIFER CHARACTERIZATION; CATCHMENT; DATA ASSIMILATION; ENSEMBLE FORECASTING; GEOSTATISTICS; GROUNDWATER-SURFACE WATER INTERACTION; HYDRAULIC CONDUCTIVITY; SPATIAL DISTRIBUTION; STREAMFLOW; SUBSURFACE FLOW;

EID: 84867097226     PISSN: 10275606     EISSN: 16077938     Source Type: Journal    
DOI: 10.5194/hess-16-287-2012     Document Type: Article

References (55)

1. Ahmed, S. and de Marsily, G.: Cokriged Estimation of Aquifer Transmissivity as an Indirect Solution of the Inverse Problem: A Practial Approach, Water Resour. Res., 29, 521-530, 1993.
2. Aubert, D., Loumagne, C., and Oudin, L.: Sequential assimilation of soil moisture and streamflow data in a conceptual rainfallrunoff model. J. Hydrol., 280, 145-161, 2003.
3. Bailey, R. T. and Baù, D. A.: Ensemble Smoother assimilation of hydraulic head and return flow data to estimate hydraulic conductivity, Water Resour. Res., 46, W12543, doi:10.1029/2010WR009147, 2010.
4. Bailey, R. T. and Baù, D. A.: Estimating spatially-variable firstorder rate constants in groundwater reactive transport systems, J. Cont. Hydrol., 122, 104-121, 2011.
5. Baú, D. A. and Mayer, A. S.: Optimal design of pump-and-treat systems under uncertain hydraulic conductivity and plume distribution, J. Cont. Hydrol., 100, 30-46, 2008.
6. Bennett, A. F.: Inverse Methods in Physical Oceanography, Cambridge Univ. Press, New York, 1992.
7. Bixio, A. C., Orlandini, S., Paniconi, C., and Putti, M.: Physically-based distributed model for coupled surface runoff and subsurface flow simulation at the catchment scale, in: Computational Methods in Water Resources, Vol. 2, Computational Methods, Surface Water systems and Hydrology, edited by: Bentley, L. R., Sykes, J. F., Brebbia, C. A., Gray, W. G., and Pinder, G. F., 1115-1122, Balkema, Rotterdam, The Netherlands, 2000.
8. Boulet, G., Kerr, Y., and Chehbouni, A.: Deriving catchment-scale water and energy balance parameters using data assimilation based on extended Kalman filtering, Hydrol. Sci., 47, 449-467, 2002.
9. Camporese, M., Paniconi, C., Putti, M., and Salandin, P.: Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow, Water Resour. Res., 45, W10421, doi:10.1029/2008WR007031, 2009.
10. Camporese, M., Paniconi, C., Putti, M., and Orlandini, S.: Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data, Water Resour. Res., 46, W02512, doi:10.1029/2008WR007536, 2010.
11. Carrera, J. and Neuman, S. P.: Estimation of aquifer parameters under transient and steady-state conditions: 1. Maximum likelihood method incorporating prior information, Water Resour. Res., 22, 199-210, 1986.
12. Carrera, J., Alcolea, A., Medina, A., Hidalgo, J., and Slooten, L. J.: Inverse problem in hydrogeology, Hydrogeol. J., 13, 206-222, 2005.
13. Chen, Y. and Zhang, D.: Data assimilation for transient flow in geologic formations via Ensemble Kalman Filter, Adv. Water Resour., 29, 1107-1122, 2006.
14. Clark, M. P., and Rupp., D. E., Woods, R. A., Zheng, X., Ibbitt, R. P., and Slater., A. G., Schmidt, J., and Uddstrom, M. J.: Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model, Adv. Water Resour., 31, 1309-1324, 2008.
15. Drécourt, J.-P., Madsen, H., and Rosbjerg, D.: Calibration framework for a Kalman filter applied to a groundwater model. Adv. Water Resour., 29, 719-734, 2006.
16. Dunne, S. and Entekhabi, D.: An ensemble-based reanalysis approach to land data assimilation, Water Resour. Res., 41, W02013, doi:10.1029/ 2004WR003449, 2005.
17. Durand, M., and Molotch., N. P., and Margulis, S. A.: A Bayesian approach to snow water equivalent reconstruction, J. Geophys. Res., 113, D20117, doi:10.1029/2008JD009894, 2008.
18. Evensen, G.: Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model, J. Geophys. Res., 97, 17905-17924, 1992.
19. Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143-10162, 1994.
20. Evensen, G.: The ensemble Kalman filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343-367, 2003.
21. Evensen, G.: Data assimilation. The Ensemble Kalman Filter, Springer-Verlag Berlin Heidelberg, 2007.
22. Evensen, G. and van Leeuwen, P. J., An ensemble Kalman smoother for nonlinear dynamics, Mon. Weather Rev., 128, 1852-1867, 2000.
23. Fu, J. and Gómez-Hernández, J. J.: Uncertainty assessment and data worth in groundwater flow and mass transport modeling using a blocking Markov chain Monte Carlo method, J. Hydrol., 364, 328-341, 2009.
24. Gailey, R. M., and Crowe., A. S., and Gorelick, S. M.: Coupled process parameter estimation and prediction uncertainty using hydraulic head and concentration data, Adv. Water Resour., 14, 301-314, 1991.
25. Gautier, Y. and Nœtinger, B.: Geostatistical parameters estimation using well test data, Oil Gas Sci. Technol., 59, 167-183, 2004.
26. Gómez-Hernández, J. J., Hendricks Franssen, H.-J., and Sahuquillo, A.: Stochastic conditional inverse modeling of subsurface mass transport: A brief review and the self-calibrating method, Stoch. Environ. Res. Risk A., 17, 319-328, 2003.
27. Hantush, M. M. and Mariño, M. A.: Estimation of spatially variable aquifer hydraulic properties using Kalman filtering, J. Hydraul. Engrg. ASCE, 123, 1027-1035, 1997.
28. Hendricks Franssen, H. J. and Kinzelbach, W.: Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem, Water Resour. Res., 44, W09408, doi:10.1029/2007WR006505, 2008.
29. Hendricks Franssen, H. J. and Kinzelbach, W.: Ensemble Kalman filtering versus sequential self-calibration for inverse modeling of dynamic groundwater flow systems, J. Hydrol., 365, 261-274, 2009.
30. Hendricks Franssen, H. J., Gómez-Hernández, J. J., Capilla, J. E., and Sahuquillo, A.: Joint simulation of transmissivity and stora-tivity fields conditional to steady-state and transient hydraulic head data, Adv. Water Resour., 23, 1-13, 1999.
31. Hendricks Franssen, H. J., Gómez-Hernández, J. J., and Sahuquillo, A.: Coupled inverse modeling of groundwater flow and mass transport and the worth of concentration data, J. Hydrol., 281, 281-295, 2003.
32. Hoeksema, R. J. and Kitanidis, P. K.: An application of the geo-statistical approach to the inverse problem in two-dimensional groundwater modeling, Water Resour. Res., 20, 1003-1020, 1984.
33. Hoeksema, R. J. and Kitanidis, P. K.: Analysis of the spatial structure of properties of selected aquifers, Water Resour. Res., 21, 563-572, 1985.
34. Jafarpour, B. and Tarrahi, M.: Assessing the performance of the ensemble Kalman filter for subsurface flow data integration under variogram uncertainty, Water Resour. Res., 47, W05537, doi:10.1029/2010WR009090, 2011.
35. Kalman, R. E.: A new approach to linear filtering and prediction problems, J. Basic Eng., 82, 35-45, 1960.
36. Keppenne, C.: Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter, Mon. Weather Rev., 128, 1971-1981, 2000.
37. Kitanidis, P. K.: On the geostatistical approach to the inverse problem, Adv. Water Resour., 19, 333-342, 1996.
38. Kitanidis, P. K. and Vomvoris, E. G.: A geostatistical approach to the inverse problem in groundwater modeling (steady state) and one-dimensional simulations, Water Resour. Res., 19, 677-690, 1983.
39. Li, B. and Yeh, T.-C. J.: Cokriging estimation of the conductivity field under variably saturated flow conditions, Water Resour. Res., 35, 3663-3674, 1999.
40. Liu, G., Chen, Y., and Zhang, D.: Investigation of flow and transport processes at the MADE site using ensemble Kalman filter, Adv. Water Resour., 31, 975-986, 2008.
41. Ngodock, H. E., and Jacobs., G. A., and Chen, M.: The representer method, the ensemble Kalman filter and the ensemble Kalman smoother: A comparison study using a nonlinear reduced gravity ocean model, Ocean Modell., 12, 378-400, 2006.
42. Orlandini, S. and Rosso, R.: Diffusion wave modeling of distributed catchment dynamics, J. Hydraul. Eng. ASCE, 1, 103-113, 1996.
43. Putti, M. and Paniconi, C.: Time step and stability control for a coupled model of surface and subsurface flow, in Proceedings of the XV International Conference on Computational Methods in Water Resources (CMWR XV), Vol. 2, 1391-1402, Elsevier, New York, 2004.
44. RamaRao, B. S., and LaVenue., A. M., de Marsily, G., and Marietta, M. G.: Pilot point methodology for automated calibration of an ensemble of conditionally simulated transmissivity field 1. Theory and computational experiments, Water Resour. Res., 31, 475-493, 1995.
45. Reichle, R. H., and McLaughlin., D. B., and Entekhabi, D.: Hydrologic data assimilation with the ensemble Kalman filter, Mon. Weather Rev., 130, 103-114, 2002.
46. Schreider, S. Y., and Young., P. C., and Jakeman, A. J.: An application of the Kalman filtering technique for streamflow forecasting in the Upper Murray Basin, Math. Computer Model., 33, 733-743, 2001.
47. Valstar, J. R., and McLaughlin., D. B., te Stroet, C. B. M., and van Geer, F. C.: A representer-based inverse method for groundwater flow and transport application, Water Resour. Res., 40, W05116, doi:10.1029/2003WR002922, 2004.
48. van Genuchten, M. T. and Nielsen, D. R.: On describing and predicting the hydraulic properties of unsaturated soils, Ann. Geophy., 3, 615-628, 1985.
49. van Leeuwen, P. J. and Evensen, G.: Data assimilation and inverse methods in terms of probabilistic formulation, Mon. Weather Rev., 124, 2898-2913, 1996.
50. Wen, W.-H., Deutsch, C. V., and Cullick, A. S.: Construction of geostatistical aquifer models integrating dynamic flow and tracer data using inverse technique, J. Hydrol., 255, 151-168, 2002.
51. Woodbury, A. D. and Smith, L.: Simultaneous inversion of hydro-geologic and thermal data 2. Incorporation of thermal data, Water Resour. Res., 24, 356-372, 1988.
52. Xie, X. and Zhang, D.: Data assimilation for distributed hydrologi-cal catchment modeling via ensemble Kalman filter, Adv. Water Resour., 33, 678-690, 2010.
53. Yeh, T.-C. J. and Zhang, J.: A geostatistical inverse method for variably saturated flow in the vadose zone, Water Resour. Res., 32, 2757-2766, 1996.
54. Yortsos, Y. C. and Al-Afaleg, N.: The permeability variogram from pressure transients of multiple wells: Theory and 1-D application, Soc. Petrol. Eng. J., 2, 328-337, 1997
55. Zhang, J. and Yeh, T.-C.J.: An iterative geostatistical inverse method for steady flow in the vadose zone, Water Resour. Res., 33, 63-71, 1997.